With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Objective To explore the predictive value of artificial intelligence (AI)-based lung nodule CT quantitative analysis for the invasion degree of lung adenocarcinoma spectrum lesions. Methods According to the invasion degree of lung adenocarcinoma spectrum lesions, patients with surgically and pathologically confirmed lung adenocarcinoma spectrum lesions from January to June 2023 in Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University were retrospectively collected and divided into a non-invasive group and an invasive group, including atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma patients in the non-invasive group, and invasive adenocarcinoma patients in the invasive group. All enrolled patients underwent chest CT before surgery, and then the lung nodules were quantitatively analyzed using an AI-based computer-aided diagnosis system to compare the related quantitative parameters of lung nodules that have been surgically removed and pathologically confirmed as lung adenocarcinoma spectrum lesions between the two groups. The relationship between various CT quantitative features and the invasion degree of lung adenocarcinoma spectrum lesions was analyzed. Results A total of 149 patients (149 lesions) were included, including 42 males and 107 females, aged 29-81 (56.35±10.75) years. There were 72 patients in the non-invasive group and 77 patients in the invasive group. Statistical differences were observed between the two groups in long diameter, short diameter, volume, surface area, mass, maximum cross-sectional area, 3D long diameter, maximum CT value, minimum CT value, average CT value, entropy, kurtosis, skewness, malignancy probability and other indicators (P<0.05). Multivariate binary logistic regression analysis showed that long diameter [OR=1.687, 95%CI (1.364, 2.085), P<0.001], average CT value [OR=1.006, 95%CI (1.002, 1.009), P=0.002], and malignancy probability [OR=1.034, 95%CI (1.005, 1.063), P=0.020] were independent risk factors for the invasion degree of lung adenocarcinoma. The predictive model combining the above parameters demonstrated optimal performance, with an area under the receiver operating characteristic curve of 0.951, sensitivity of 0.818, and specificity of 0.972. Using a Nomogram to quantify the three independent risk factors, the cross-validation was performed to evaluate the stability of the model, and the average C-index of cross-validation was 0.950, with each fold C-index >0.75, indicating that the prediction performance of the model was stable, and the calibration curve and decision curve indicated good predictive performance. Conclusion The visualization prediction model constructed by AI-based quantitative analysis of lung nodules in CT demonstrates significant discriminative effectiveness in the assessment of invasiveness in lung adenocarcinoma spectrum lesions. This visualization prediction model can provide a quantitative decision-making basis for the preoperative identification of the degree of invasiveness in lung adenocarcinoma spectrum lesions.

ObjectiveTo explore the effect of piercing electroacupuncture on ankle muscle strength, dynamic balance and subjective stability in patients with functional ankle instability (FAI). MethodsA total of 48 patients with FAI admitted to the Affiliated Hospital of Traditional Chinese Medicine, Shandong Second Medical University from April, 2024 to July, 2025 were enrolled and randomly divided into control group (n = 24) and experimental group (n = 24). The control group received multimodal functional training, while the experimental group received piercing electroacupuncture in addition, for six weeks. Relative peak torque of ankle plantarflexion, dorsiflexion, inversion and eversion were measured with isokinetic muscle strength testing system before and after treatment, while Y-Balance Test (YBT) and Cumberland Ankle Instability Tool (CAIT) were adopted for evaluation. ResultsOne case dropped out in each group. Relative peak torque of ankle plantarflexion, dorsiflexion, inversion and eversion at each angular velocity, the normalized distance in each direction and composite score of YBT, as well as the score of CAIT increased in both groups after treatment (|t| > 2.891, P < 0.01), while all the indicators were better in the experimental group than in the control group (|t| > 2.104, P < 0.05), except dorsiflexion muscle strength. ConclusionCombination of piercing electroacupuncture can further improve perimalleolar muscle strength, dynamic balance and subjective stability in patients with FAI.

Objective To explore the wearing status and actual noise reduction effect of hearing protectors among noise workers in a typical automobile manufacturing enterprise. Methods In April 2024, an occupational hazard factor testing was carried out in an automobile manufacturing industry, and at the same time, the hearing protection fit test was conducted for noise workers. Intervention and guidance were provided to those who did not pass the minimum standard of baseline PAR. The difference in PAR between baseline and post-intervention was compared, and the effectiveness of hearing protector wearing method training was evaluated. Results The exceeding rate of the company's noise operation post was 50.77% (66/130). The baseline PAR of the subjects with working experience of less than 15 years and wearing hearing protectors throughout noisy work was higher, and the differences were statistically significant (P<0.05). Compared with those with 80dB≤L_{EX, 8h}<85dB, more research subjects with L_{EX, 8h}≥85dB failed baseline PAR (39.13%). After intervention, the PAR of the subjects who did not pass the minimum standard of baseline PRA increased from 2.0 (0.0, 5.3) to 17.0 (14.8, 20.0), and the protection level was significantly improved, and the difference was statistically significant (P<0.01). Conclusion The individual fit test of hearing protector is an important means to evaluate the actual noise reduction level of hearing protector and guide the selection of hearing protection models. Corporate training can help improve the PAR of hearing protectors.

Traditional treatment for type 1 diabetes mellitus （T1DM） primarily involves insulin replacement， yet some patients encounter issues such as significant blood glucose fluctuations， high risk of hypoglycemia， and weight gain. In recent years， the adjuvant therapeutic role of non-insulin hypoglycemic drugs in T1DM has gradually gained attention. This article reviews the mechanisms of action and clinical research progress of five types of non-insulin hypoglycemic drugs in the treatment of T1DM： amylin analogues （pramlintide）， biguanides （metformin）， sodium-glucose co-transporter 2 inhibitor， dipeptidyl peptidase-4 inhibitor， and glucagon-like peptide-1 receptor agonist. It is found that these drugs can enhance clinical benefits for T1DM patients by improving insulin sensitivity， delaying gastric emptying， promoting urinary glucose excretion， and regulating incretin levels， thereby reducing glycated hemoglobin levels， decreasing insulin dosage， and managing body weight. Simultaneously， these drugs also present limitations such as low patient compliance due to complex dosing regimens， increased risk of diabetic ketoacidosis， and heterogeneity in glycemic control. Future research could focus on developing individualized treatment strategies， combining pharmacogenomics with novel biomarkers to precisely identify subpopulations of patients who may benefit， and delving into the potential value of these drugs in delaying diabetic vascular complications and improving patients’ quality of life.

Objective To analyze the increase in hearing thresholds in different frequency bands of workers exposed to different characteristic noises and the influencing factors of binaural high-frequency hearing loss, and to provide theoretical support for occupational health monitoring in enterprises. Methods In 2021, noise-exposed workers were selected from typical noise-exposing enterprises as research subjects, and their occupational health examination data were tracked for three consecutive years. According to the on-site detection from 2021 to 2023, the cumulative noise exposure (CNE) was calculated. The chi-square test was used to compare the increase of the hearing threshold of workers exposed to different characteristic noises in different frequency bands. A generalized estimation equation was constructed to explore the influencing factors of high-frequency hearing loss in the research subjects. Results The age of the study subjects was 23-54 years old, with 1.6-20.1 years of working experience, mainly distributed in manufacturing workshops, technical workshops and public works workshops. The median LAeq,8h was 85.90 (84.5, 86.1) dB(A), and the average CNE was 94.3±2.6 dB(A). The number of people with increased hearing thresholds in each frequency band was 0.5kHz>6kHz>4kHz>3kHz>1kHz>2kHz. There were statistically significant differences in the increase of hearing thresholds in each frequency band, binaural speech frequency impairment, and high-frequency hearing loss among subjects with different characteristics (P<0.05). The detection rate of binaural speech frequency hearing loss was the highest in subjects aged>45 years, with a length of service of>15 years, and with a CNE>96dB(A). The detection rate of binaural high-frequency hearing loss was the highest in subjects aged 35-40 years, with a length of service of 5-10 years, and with a CNE>96dB(A).In addition, the detection rate of binaural speech frequency and high-frequency hearing loss increased with the increase of CNE(P<0.05). The GEE model showed that age>40 years and CNE>96dB(A) were risk factors for high-frequency hearing loss in both ears. Conclusion With increasing age and CNE, hearing thresholds rise across all frequency bands, elevating the risk of binaural speech-frequency and high-frequency hearing loss, thereby increasing the likelihood of occupational noise-induced deafness.

Objective To examine the impact of climate, land use and their interaction effect on the population density of Oncomelania hupensis. Methods O. hupensis snail surveillance data in Yunnan Province from 2013 to 2022 were obtained from the National Information System for Parasitic Diseases Prevention and Control of Chinese Information System for Disease Control and Prevention, and the density of living snails was calculated in each county-level administrative district. Climate data were obtained from the China Meteorological Forcing Dataset version 2.0, and land use data were captured from the China Land Cover Dataset. The raster climate data and land use data were clipped to the administrative border of Yunnan Province in the R package version 4.5, and the climate data were resampled to a spatial resolution of 30 m × 30 m consistent with the land use data using bilinear interpolation. Annual meteorological indicators were extracted from each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022, including average temperature, maximum temperature, minimum temperature, temperature difference, average precipitation rate, maximum precipitation rate, minimum precipitation rate, downward shortwave radiation flux, downward longwave radiation flux, air pressure, wind speed, specific humidity and relative humidity. Annual land use structure and landscape pattern index in each county-level schistosomiasis surveillance administrative area in Yunnan Province from 2013 to 2022 were extracted and calculated, including the proportion of areas occupied by each type of land use, patch density, largest patch index, landscape separation index, landscape fragmentation index, landscape segmentation index, contagion index, patch cohesion index, edge density, and shape index. Land use entropy index was introduced to represent the degree of habitat fragmentation and ecological niche breadth. The density of living snails and environmental factors were subjected to rank correlation analysis, and variables with no statistically significant correlation (P>0.1) were excluded. Then, a correlation coefficient matrix was constructed, and |r_s| of 0.85 and greater was defined as the threshold of high collinearity. For highly correlated variables in each group, indicators with a more biological explanatory power or a better statistical efficiency were retained. A generalized additive model (GAM) was constructed with O. hupensis snail density as a response variable and screened environmental variables as independent variables, and the model performance was evaluated using the variance explained. Results The area occupied by O. hupensis snail habitats was 1 056 to 1 680 hm² in Yunnan Province from 2013 to 2022, appearing an overall tendency towards a decline; however, the density of living snails appeared a tendency towards a slight rise. Based on the correlation coefficient matrix, 14 core explanatory variables were retained, including farmland, water bodies, bare land, impervious surface, landscape separation index, landscape fragmentation index, edge density, shape index, land use entropy index, downward longwave radiation flux, downward shortwave radiation flux, maximum temperature, minimum precipitation rate and specific humidity. Two types of GAM were constructed, including the main-effects model without interaction terms and the interaction model with interaction terms. The corrected Akaike information criterion (AICc) of the optimal main-effects model was −285.223, and the core variables included bare land, impervious surface, landscape separation index, edge density, shape index, land use entropy index and downward shortwave radiation flux. Following introduction of second-order interaction terms, the AICc value of the optimal interaction model was −345.526, and the significant interaction combinations included: farmland × impervious surface, farmland × landscape separation index, farmland × shape index, farmland × land use entropy index, impervious surface × landscape separation index, impervious surface × edge density, and shape index × downward shortwave radiation flux. The proportion of the main-effects model explaining the variation of O. hupensis snail density was 39.8%, and the explanatory power of GAM increased to 79.0% following introduction of interaction terms. Conclusion Landscape separation index, edge density, shape index and other landscape pattern indicators pose significant impacts on the intensity and direction of changes in O. hupensis snail density in Yunnan Province.

Objective To assess the risk of hearing loss caused by occupational noise exposure in workers in an automobile manufacturing plant in Tianjin, China, and to perform risk management. Methods Occupational health field investigation and noise exposure measurements were conducted from July to December 2023, and physical examination data were collected. ISO 1999:2013(E) Acoustics-Estimation of Noise-Induced Hearing Loss and WS/T 754-2016 ^“Guidelines for Risk Management of Occupational Disease Hazards Caused by Noise^” were used to predict the risk of high-frequency hearing loss and occupational noise induced deafness for operational workers and make a risk classification. Results The noise intensity of each workshop was 79.4 to 95.5 dB(A), and the maximum noise intensity of welding and stamping exceeded the standard. The results of the assessment showed that the noise level remained unchanged, and the risk of HFHL and ONID in workers increased as the predicted age and length of service increased. It was predicted that after the age of 40, the maximum risk of hearing loss in welding workers would be high risk, and the risk of stamping workers would be at higher risk, suggesting that welding and stamping were the key control posts of noise hazards in the enterprise. The N50 prediction values of permanent hearing threshold displacement caused by potential noise at all frequencies for final assembly and painting workers were lower than the measured values. Conclusion The consequences of hearing loss for workers in the welding and stamping shop noise operations at this automobile manufacturing plant are relatively serious and require risk management.

Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

Objective:To investigate the relationship between artificial radionuclide 137Cs and stable cesium (Cs) in wild edible fungi and seek potential correlations. Methods:A total of 30 samples, including the caps (with gills) and stipes of wild edible fungi, were collected from the northeastern region of China. The measurement and analysis of 137Cs were conducted following recommended procedures in GB/T 16145-2022, and stable Cs was determined using inductively coupled plasma mass spectrometry (ICP-MS). Then, the correlation analysis of data on 137Cs and stable Cs was performed using SPSS 11.5 software, and scatter plots were prepared using the Origin 21.0 software. Results:The fungi caps exhibited a specific activity of 137Cs ranging from 0.52 to 55.9 Bq/kg (dry weight) and a stable Cs content from 0.069 mg/kg to 16.2 mg/kg (dry weight). The stipes showed a specific activity of 137Cs ranging from 0.53 Bq/kg to 101 Bq/kg (dry weight) and a stable Cs content from 0.075 to 11.5 mg/kg (dry weight). These data revealed a significant correlation between the specific activity of 137Cs and the stable cesium content in all samples including caps and stipes, with correlation coefficients r of 0.956, 0.912, and 0.931, respectively, and all significant levels P < 0.01. The ratios of the specific activity of 137Cs to stable Cs content varied from 2.09 Bq/kg to 20.1 Bq/kg (dry weight), with an average of 10.7 Bq/kg (dry weight). Conclusions:Wild edible fungi fail to distinguish between 137Cs and stable cesium when absorbing Cs elements from their growing substrates. Furthermore, there is a strong correlation between the specific activity of 137Cs and stable Cs content. In the case of exogenous 137Cs contamination, the ratio of the specific activity of 137Cs and stable Cs content will significantly change. Therefore, an increase in the ratio can be used as a reference for identifying 137Cs contamination events.